Researchers have uncovered a vital insight: having too much information can actually impede the decision-making process. This discovery has significant implications, particularly in public health, where presenting information in a clear and focused manner could lead to better decision-making. The use of AI chatbots is also suggested as a potential tool for delivering personalized advice to improve decision-making effectiveness.
The research, conducted at the Stevens Institute of Technology, indicates that even a slight surplus of information can negatively impact decision-making effectiveness. When people are faced with difficult choices, they tend to instinctively seek out a lot of information. However, recent findings published in Cognitive Research: Principles and Implications indicate that this abundance of data can deteriorate, rather than improve, decision quality.
Associate Professor Samantha Kleinberg, the lead author of the study and a computer scientist at Stevens Institute of Technology, notes, “It’s a common misconception that more information equates to better decisions. However, our research shows that the opposite can be true.”
In their study, researchers generally use simple diagrams or causal models to demonstrate how different factors interact to produce specific outcomes. These models work well in hypothetical scenarios, such as alien dance-offs, where people have no pre-existing biases or notions. In these cases, people make sound decisions based on the given information.
However, Kleinberg’s study reveals that in everyday situations, such as making nutrition-related decisions, people’s decision-making skills significantly diminish. “People’s pre-existing knowledge and beliefs tend to distract them from the causal model they are presented with, complicating the decision-making process,” explains Kleinberg.
Furthering their 2020 study, Kleinberg, alongside cognitive psychologist Jessecae Marsh from Lehigh University, conducted experiments to examine how decision-making varies with different causal models across a range of real-life topics. They found that people generally understand how to use causal models, but even a simple model becomes ineffective with the addition of non-essential details.
Kleinberg remarks, “It’s astonishing how even a small amount of extra information can severely hinder decision-making. It can be as detrimental as having no information at all.”
The research also highlights that when the relevant causal information in a model is emphasized, people’s decision-making abilities improve significantly. “This indicates that the challenge lies not in the amount of information, but in identifying which parts are crucial,” Kleinberg adds.
The findings are particularly relevant in public health and other fields, suggesting that for educational messages to be effective, they must be distilled to their most essential elements. Kleinberg notes, “Overloading people with information can actually prevent them from making well-informed decisions.”
Interestingly, even when participants in the study had the choice of more or less information, those who opted for more information tended to make worse decisions. Kleinberg concludes, “This demonstrates the need for simple, targeted causal models for effective decision-making.”
The potential of AI chatbots to provide tailored health or nutritional advice to individuals, based on complex causal models, is also suggested. These AI tools could focus on highlighting only the most relevant information for each individual.
This research was supported by the James S. McDonnell Foundation and the National Science Foundation and published on 30 August 2023 in Cognitive Research: Principles and Implications, under the title “Less is more: information needs, information wants, and what makes causal models useful,” authored by Samantha Kleinberg and Jessecae K. Marsh. The DOI for the publication is 10.1186/s41235-023-00509-7.
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Frequently Asked Questions (FAQs) about Decision-making efficiency
How does excess information affect decision-making?
Excess information can significantly impair decision-making. The study shows that even a small amount of additional data can complicate the decision-making process, making it as ineffective as if there were no information available.
What are the implications of this study in public health?
In public health, the study suggests that educational messages and advisories should be concise and focus only on essential information. Overloading individuals with data can hinder their ability to make informed decisions.
Can AI chatbots help improve decision-making?
Yes, AI chatbots could potentially improve decision-making by personalizing and tailoring advice. By feeding complex causal models into AI systems, they can highlight the most relevant information for individuals, aiding in more efficient decision-making.
What was unique about the research methodology in this study?
The research used simple diagrams or causal models to understand decision-making. These models are effective in hypothetical situations but tend to fail in real-life scenarios due to pre-existing biases and knowledge, which was a key focus of the study.
What is the main conclusion of the study conducted by Samantha Kleinberg?
The main conclusion of the study is that more information is not always beneficial for making smart decisions. Focused, relevant information is key to effective decision-making, as highlighted in the research findings.
More about Decision-making efficiency
- Cognitive Research: Principles and Implications
- Stevens Institute of Technology Research
- James S. McDonnell Foundation
- National Science Foundation Grants
- AI and Decision-Making Research
5 comments
Good article but I think it oversimplifies things a bit? Not all decisions are the same, and some require more info than others, right?
this makes so much sense, I get so overwhelmed with all the data thrown at us daily. Good to know it’s not just me!
I read a similar study last year, its crazy how our brains can’t process too much info at once, makes you think about how we consume news and media these days.
Wow, pretty interesting stuff! Never thought that too much info can actually be bad for decision-making, always thought more info was better??
interesting article but how would AI chatbots really know what’s important for each person? sounds a bit too techy and complicated for my taste